Systems and methods for generating consumption probability metrics

ABSTRACT

A consumption probability metric may be generated for a media asset. An aggregated forecast predicting user consumption of a media asset is received. A plurality of probabilities, each corresponding to a user of a plurality of users, is received, each indicating how likely a respective user is to consume the media asset. A weight for the plurality of users is calculated representing a ratio of the total number of users to a number of users in the plurality of users. A disaggregated forecast predicting user consumption of a media asset is determined based on the weight for the plurality of users and the plurality of probabilities. A modification factor is computed based on the aggregated forecast and the disaggregated forecast. A metric is generated that includes a plurality of user identifiers associated with the plurality of users and a plurality of modified probabilities each modified by the modification factor.

BACKGROUND

Various parties have the need to accurately predict a number of usersthat will consume a specific program. For example, a corporation maydesire to reach as wide of an audience as possible (e.g., with anadvertisement). Various ways currently exist to predict a number ofusers that will consume a specific program. For example, some providersmonitor user equipment devices in various households in order toidentify channels that the user equipment devices are tuned to, andbased on this information, extrapolate an approximate number of usersthat consumed a particular program or channel. From that information,these providers can predict a number of people that will consume afuture program. However, these providers are not able to efficientlypredict cross-sections of users (e.g., users having specific preferencesand/or characteristics) that will consume the media asset. Furthermore,these systems may not be able to accurately predict a number of uniqueusers that will consume a specific set of media assets as well as thenumber of times a user may be exposed to multiple media assets, forexample, in an advertisement campaign.

SUMMARY

Therefore, systems and methods are disclosed herein for generatingconsumption probability metrics for media assets. The consumptionprobability metrics are used in a unified forecasting approach. Thisapproach combines aggregated metrics (e.g., national ratings) anddisaggregated viewership (e.g., user-level probabilities of viewingcontent) to determine viewership of a specific user-defined target(e.g., users that like trucks) and reach (e.g., unique number of usersthat will consume a specific media asset).

In some aspects, a consumption probability metric may be generated for amedia asset. A first value representing a first number of users that arepredicted to consume a media asset is received, the value being aportion of a total number of users (e.g., aggregated forecast). Aplurality of probabilities, each corresponding to a user of a pluralityof users, is received, each of the received probabilities indicating howlikely a respective user is to consume the media asset. A weight for theplurality of users is calculated, the weight representing a ratio of thetotal number of users to a number of users in the plurality of users. Asecond value representing a second number of users that are predicted toconsume the media asset is determined (e.g., disaggregated forecast)based on (1) the weight for the plurality of users and (2) the pluralityof probabilities. A modification factor is computed based on the firstnumber of users and the second number of users. A metric is generatedthat includes (1) a plurality of user identifiers associated with theplurality of users and (2) a plurality of modified probabilities, eachmodified probability of the plurality of modified probabilities modifiedby the modification factor.

A viewership forecasting application may be used to perform the actionsdescribed with respect to generating the consumption probabilitymetrics. The viewership forecasting application may reside on one ormore devices and may be accessed from those devices, as well as otherconnected devices.

In some embodiments, the viewership forecasting application may receivea prediction of a number of users that will consume a media asset (e.g.,a number of households of a total population of households that aremonitored for media consumption activity). Specifically, the viewershipforecasting application may receive a first number of users that arepredicted to consume a media asset, where the first number of users is aportion of a total number of users. For example, the viewershipforecasting application may receive from a provider such as The NielsenCompany information that indicates that ten million out of one hundredmillion households are predicted to view an episode of the show “The BigBang Theory.”

The viewership forecasting application may have access to one or moresources of information that includes a plurality of user profilesassociated with a plurality of users, respectively. The user profileinformation may include user preference information that may be used todetermine a probability that a particular user will consume a particularmedia asset. The viewership forecasting application may retrieve theprobabilities. Specifically, the viewership forecasting application mayretrieve a plurality of probabilities, each corresponding to a user in aplurality of users, where each probability indicates how likely arespective user is to consume the media asset. In some embodiments, theviewership forecasting application may retrieve the user profiles andcalculate the probabilities based on information (e.g., user preferencesand characteristics) stored in the profiles.

The viewership forecasting application may calculate a weight for theplurality of users based on the ratio of the number of users in theplurality of users to the total number of users (e.g., a totalpopulation of households that are monitored for media consumptionactivity). Specifically, the viewership forecasting application maycalculate a weight for the plurality of users, where the weightrepresents a ratio of the total number of users to a number of users inthe plurality of users. For example, if the total number of users in thepopulation is 100,000 and the number of users in the plurality of usersis 100, each user in the plurality of users will have a weight of 1,000(i.e., 100,000 divided by 100).

The viewership forecasting application may determine a second number ofusers that are predicted to consume the media asset (e.g., disaggregatedforecast). The viewership forecasting application may use the weight forthe plurality of users, the number of users in the first plurality ofusers, and the plurality of probabilities to make the determination.Specifically, the viewership forecasting application may determine,based on (1) the weight for the plurality of users and (2) the pluralityof probabilities, a second number of users that are predicted to consumethe media asset. For example, the viewership forecasting application mayretrieve for each in the plurality of users a corresponding probabilityand calculate a sum of all the probabilities. The viewership forecastingapplication may determine the product of the sum and weight for theplurality of users to arrive at the second number of users.

The viewership forecasting application may compute a ratio between thefirst number of users and the second number of users, therebyidentifying a ratio of the aggregated forecast to disaggregatedforecast. Specifically, the viewership forecasting application maycompute, based on the first number of users and the second number ofusers, a modification factor for the first media asset. For example, theviewership forecasting application may divide the first number of usersby the second number of users.

The viewership forecasting application may generate a metric based onthe plurality of probabilities using the modification factor.Specifically, the viewership forecasting application may generate, forthe media asset, a metric comprising (1) a plurality of user identifiersassociated with the plurality of users and (2) a plurality of modifiedprobabilities, where each modified probability of the plurality ofmodified probabilities is modified by the modification factor. Forexample, the viewership forecasting application may multiply eachprobability by the modification factor and store the result as part ofthe metric. The generated metric may be referred to as consumptionprobability metric.

In some embodiments, the viewership forecasting application may use thegenerated consumption probability metric to forecast viewership of across-section of users (e.g., users that are known to enjoy chocolateproducts). Specifically, the viewership forecasting application mayreceive a characteristic associated with a group of users (e.g.,enjoyment of chocolate). The viewership forecasting application maycompare the characteristic with each of a plurality of profilesassociated with the plurality of users and select, based on thecomparing, a set of user identifiers from the plurality of useridentifiers corresponding to those profiles that match thecharacteristic. The viewership forecasting application may determine,using a portion of the metric associated with the set of useridentifiers, an amount of users that are likely to consume the mediaasset.

For example, an advertiser may be considering running an advertisementfor a chocolate candy. It would be useful for the advertiser to know howmany users that are known to like chocolate are predicted to watch aspecific show. Thus, enjoyment of chocolate may be a characteristic thatis compared with the data in the user profiles and based on the numberof profiles that match that characteristic, the viewership forecastingapplication may determine the number of viewers that enjoy chocolatethat are predicted to watch a specific show. Those numbers may becompared with the numbers for other shows in order to choose the showduring which the advertisement will be played.

In some embodiments, the viewership forecasting application maydetermine the amount of users that are likely to consume the media assetusing the following actions. The viewership forecasting application maycalculate a sum of modified probabilities that are associated with theportion of the user metric and multiply the sum by the weight. Forexample, if there are five users in the plurality of users with themodified probabilities of 0.6, 0.7, 0.4, 0.3, and 0.5 and the weight ofeach user is twenty million, the viewership forecasting application maycalculate a sum of all the probabilities (i.e., 2.5) and multiply thesum by the weight (i.e., 25 million) to arrive at the result of 62.5million users.

In some embodiments, the viewership forecasting application maydetermine a number of unique users that are likely to consume anadvertisement associated with a specific advertiser using the generatedconsumption probability metric. Specifically, the viewership forecastingapplication may receive, from an advertiser, a value representing anumber of advertisements associated with the advertiser that are to beplayed during presentation of the media asset and determine, based on(1) the value representing the number of advertisements and (2) theplurality of modified probabilities and (3) the weight, a number ofunique users that are likely to consume any advertisement that is both(1) associated with the advertiser and (2) is to be played during thepresentation of the media asset.

For example, for each user, the viewership forecasting application mayretrieve the modified probability (e.g., 0.4) and subtract that numberfrom one. The result of the subtraction operation (e.g., 0.6) is raisedto the power equal to the number of advertisements associated with theadvertisers that are run during the media asset (e.g., 0.6 raised to thepower of two equals 0.36). This value is subtracted from one (e.g., oneminus 0.36 equals 0.64). These actions determine a probability that auser consumed at least one advertisement of two advertisementsassociated with the advertiser that ran during the media asset. Theviewership forecasting application may multiply the resultingprobability by the weight to determine the probability that a uniqueuser will consume at least one advertisement run within the media assetconsidered in the calculation (e.g., 0.64 multiplied by 25 million fromthe example above to arrive at the value of 16 million). The viewershipforecasting application may make a similar determination for all usersthat are being considered and calculate a sum of the result to determinehow many unique users are predicted to consume an advertisement (i.e.,determine reach). It should be noted that these values may be calculatedover a plurality of media assets (e.g., media assets in an advertisingcampaign).

In some embodiments, the viewership forecasting application maydetermine the average number of advertisement exposures per user.Specifically, the viewership forecasting application may determine basedon the total number of users and the number of unique users that arelikely to consume any advertisement associated with the advertiser anumber of times that each user consumed an advertisement that isassociated with the advertiser. For example, if the number of uniqueusers that are predicted to consume an advertisement is 50 million outof 100 million who actually consumed the media asset, the viewershipforecasting application may calculate the number of times on averageeach user consumed an advertisement as two (i.e., 100 million divided by50 million).

In some embodiments, the viewership forecasting application may receivethe first number of users that are predicted to consume the media assetwith the following actions. The viewership forecasting application mayreceive a media asset identifier associated with the media asset. Theviewership forecasting application may transmit, to an audiencemeasurement provider, a request for the first number of users that arepredicted to consume the media asset, where the request includes themedia asset identifier, and receive, in response to the request, thefirst number of users that are predicted to consume the media asset. Forexample, a user may provider the viewership forecasting application witha media asset identifier for consumption probability metric generation.The viewership forecasting application may transmit the media assetidentifier to an audio measurement service (e.g., Nielsen Company) andreceive back a number of users (e.g., ten million) predicted to consumethe media asset associated with the media asset identifier.

The viewership forecasting application may receive the plurality ofprobabilities with the following actions. The viewership forecastingapplication may select a service that stores a plurality of profilesassociated with the plurality of users and transmit, to a profile serverassociated with the service, a request for the plurality ofprobabilities, the request including the media asset identifier. Theviewership forecasting application may receive, in response to therequest, the plurality of probabilities. For example, the viewershipforecasting application may select a service storing user viewershipdata (e.g., anonymized data originally derived from providers likeNetflix®, Hulu®, or another suitable provider). The viewershipforecasting application may select a provider based on a number of usersfor which profiles exist, the amount of data in those profiles, oranother suitable criterion. In some embodiments, the viewershipforecasting application may select multiple providers. The viewershipforecasting application may transmit a request for the plurality ofprobabilities.

For example, the viewership forecasting application may transmit a mediaasset identifier associated with the media asset requesting probabilityfor each user consuming the media asset and receive in response theappropriate probabilities. In some embodiments, the viewershipforecasting application may receive a plurality of user profiles and,based on an algorithm, determine a probability of each user associatedwith a corresponding profile consuming the media asset.

In some embodiments, the viewership forecasting application maydetermine, based on (1) the weight for the plurality of users and (2)the plurality of probabilities, the second number of users that arepredicted to consume the media asset with the following actions. Theviewership forecasting application may compute a sum of the plurality ofprobabilities and multiply the sum of the plurality of probabilities bythe weight. For example, if there are a total of one hundred users inthe plurality of users and each represents one million users (e.g.,weight of one million) and the probabilities of the one hundred usersadd up to twenty-five, the viewership forecasting application maycalculate the second number of users that are predicted to consume themedia asset by multiplying twenty-five by one million (e.g., resultingin a value of twenty-five million).

In some embodiments, the viewership forecasting application may computethe modification factor for the first media asset by dividing the firstnumber of users by the second number of users. For example, if a Nielsenrating for a media asset is predicted to be twenty million users of onehundred million users (aggregated forecast) and the number of usersdetermined from the plurality of probabilities is twenty-five millionusers, the modification factor may be calculated as twenty-five milliondivided by twenty million (i.e., 1.25).

In some embodiments, the viewership forecasting application maygenerate, for the media asset, the metric comprising (1) a plurality ofuser identifiers associated with the plurality of users and (2) aplurality of modified probabilities with the following actions. Theviewership forecasting application may select a first probability of theplurality of probabilities and compute a product of the firstprobability and the modification factor. The viewership forecastingapplication may store, in a data structure associated with the metric,the product of the first probability and the modification factor and theuser identifier associated with the first probability. For example, ifthe modification factor is 1.25 and the first probability is 0.5, theviewership forecasting application may determine that the modifiedprobability is 0.625 (i.e., 1.25 multiplied by 0.5). The viewershipforecasting application may identify the user associated with the firstprobability and store the value of 0.625 and a user identifierassociated with the user in a data structure generated for the metric.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages of the disclosure will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative example of a data structure that may storea received plurality of probabilities and a modified plurality ofprobabilities in accordance with some embodiments of the disclosure;

FIG. 2 shows an illustrative example of a display screen for use inaccessing media content in accordance with some embodiments of thedisclosure;

FIG. 3 shows another illustrative example of a display screen for use inaccessing media content in accordance with some embodiments of thedisclosure;

FIG. 4 is a block diagram of an illustrative user equipment device inaccordance with some embodiments of the disclosure;

FIG. 5 is a block diagram of an illustrative media system in accordancewith some embodiments of the disclosure;

FIG. 6 is a flowchart of illustrative actions for generating aconsumption probability metric for a media asset in accordance with someembodiments of the disclosure;

FIG. 7 is a flowchart of illustrative actions for using a consumptionprobability metric to determine a number of users associated with acharacteristic that are predicted to consume a media asset in accordancewith some embodiments of the disclosure; and

FIG. 8 is a flowchart of illustrative actions for using a consumptionprobability metric to determine a unique reach for one or moreadvertisements within a media asset in accordance with some embodimentsof the disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed herein for generating consumptionprobability metrics for media assets. A viewership forecastingapplication may be used to execute the actions discussed herein. Theviewership forecasting application may run on one or more hardwaredevices that include control circuitry (e.g., servers, user equipmentdevices, or a combination of the two types of devices). In someembodiments, the viewership forecasting application may interface with amedia guidance application on user devices and server devices.

A consumption probability metric may be generated for a media asset. Afirst value representing a first number of users that are predicted toconsume a media asset is received, the value being a portion of a totalnumber of users (e.g., aggregated forecast). A plurality ofprobabilities, each corresponding to a user of a plurality of users, isreceived, each of the received probabilities indicating how likely arespective user is to consume the media asset. A weight for theplurality of users is calculated, the weight representing a ratio of thetotal number of users to a number of users in the plurality of users. Asecond value representing a second number of users that are predicted toconsume the media asset is determined (disaggregated forecast) based on(1) the weight for the plurality of users and (2) the plurality ofprobabilities. A modification factor is computed based on the firstnumber of users and the second number of users. A metric is generatedthat includes (1) a plurality of user identifiers associated with theplurality of users and (2) a plurality of modified probabilities, eachmodified probability of the plurality of modified probabilities ismodified by the modification factor.

In some embodiments, the viewership forecasting application may receivea prediction of a number of users that will consume a media asset (e.g.,from a total population of households that are monitored for mediaconsumption activity). Specifically, the viewership forecastingapplication may receive a first number of users that are predicted toconsume a media asset, where the first number of users is a portion of atotal number of users. For example, the viewership forecastingapplication may transmit a request to a provider for the prediction. Therequest may include the media asset identifier of the media asset.

The viewership forecasting application may have access to one or moresources of information that includes a plurality of user profilesassociated with a plurality of users, respectively. The user profileinformation may include user preference information that may be used todetermine a probability that a particular user will consume a particularmedia asset. The viewership forecasting application may retrieve theprobabilities. Specifically, the viewership forecasting application mayretrieve a plurality of probabilities, each corresponding to a user in aplurality of users, where each probability indicates how likely arespective user is to consume the media asset. For example, theviewership forecasting application may transmit, to a server, a requestthat includes the media asset identifier, requesting a probability thateach user will consume the media asset associated with the media assetidentifier. The provider may calculate the probability based on the userprofiles and transmit to the viewership forecasting application thoseprobabilities.

In some embodiments, the viewership forecasting application may retrievethe user profiles and calculate the probabilities based on thoseprofiles. The viewership forecasting application may transmit a requestto a user profile provider for anonymized user profile information andcalculate, based on the received profile information, respectiveprobabilities of how likely each respective user is to consume the mediaasset. The viewership forecasting application may store the received orcalculated probabilities in data structure 100 (FIG. 1). Each field 102(FIG. 1) may include a user identifier associated with a correspondingprofile, and each field 104 (FIG. 1) may include the probabilityassociated with that user identifier. Although data structure 100 storeseach probability as a number between one and zero, it should be notedthat the probability may be stored as a fraction, a percentage, or anyother suitable indication.

The viewership forecasting application may calculate a weight for theplurality of users based on the ratio of the number of users in theplurality of users to the total number of users (e.g., a totalpopulation of households that are monitored for media consumptionactivity). Specifically, the viewership forecasting application maycalculate a weight for the plurality of users, where the weightrepresents a ratio of the total number of users to a number of users inthe plurality of users. For example, the viewership forecastingapplication may divide the total number of users by the plurality ofusers to determine the weight.

The viewership forecasting application may determine a second number ofusers that are predicted to consume the media asset (i.e., disaggregatedforecast). The viewership forecasting application may use the weight forthe plurality of users, the number of users in the first plurality ofusers, and the plurality of probabilities to make the determination.Specifically, the viewership forecasting application may determine,based on (1) the weight for the plurality of users and (2) the pluralityof probabilities, a second number of users that are predicted to consumethe media asset. For example, the viewership forecasting application mayinitialize a variable for a sum of the plurality of probabilities. Theviewership forecasting application may iterate through each user in theplurality of users and retrieve for each user a correspondingprobability. The viewership forecasting application may add, at eachiteration, the corresponding probability to the variable for the sum.The viewership forecasting application may determine the product of thesum and weight for the plurality of users to arrive at the second numberof users. The calculation may be represented by the following equation:

$\begin{matrix}{{\overset{\sim}{A}}_{t} = {\sum\limits_{i}{w_{i}P_{it}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

where parameters of equation 1 are defined as follows:Ã_(t) a number of users predicted to consume media asset tw_(i) weight of each user iP_(it) probability that each user i will consume media asset t.

The viewership forecasting application may compute a ratio between thefirst number of users and the second number of users, therebyidentifying a ratio of the aggregated forecast to the disaggregatedforecast. Specifically, the viewership forecasting application maycompute, based on the first number of users and the second number ofusers, a modification factor for the first media asset. For example, theviewership forecasting application may divide the first number of usersby the second number of users. The calculation may be represented by thefollowing equation:

λ_(t) =A _(t) /Ã _(t)  Equation 2

where parameters of equation 2 are defined as follows:λ_(t) modification factor for title tA_(t) a prediction of a number of users that will consume media asset tas received (aggregated forecast)Ã_(t) a number of users predicted to consume a media asset calculated inEquation 1 (disaggregated forecast).

The viewership forecasting application may generate a metric based onthe plurality of probabilities using the modification factor.Specifically, the viewership forecasting application may generate, forthe media asset, a metric comprising (1) a plurality of user identifiersassociated with the plurality of users and (2) a plurality of modifiedprobabilities, where each modified probability of the plurality ofmodified probabilities is modified by the modification factor. Forexample, the viewership forecasting application may iterate through eachuser 102 in data structure 100 and for each user 102 retrieveprobability 104. The viewership forecasting application may calculate aproduct of the retrieved probability and the modification factor. Theviewership forecasting application may store a user identifier (e.g.,user identifier 152) and the resulting product (e.g., probability 154)in data structure (150) corresponding to the metric.

The generated metric may be referred to as a consumption probabilitymetric. The modification of each probability may be represented by thefollowing equation:

π_(it)=λ_(t) p _(it)  Equation 3

where parameters of equation 3 are defined as follows:π_(it) probability modified by the modification factor that user i willconsume title tλ_(t) modification factor for title tp_(it) retrieved probability that user i will consume title t.

It should be noted that the viewership forecasting application mayperform an error check on the calculations in Equation 1, Equation 2,and Equation 3 by the following equation:

$\begin{matrix}{A_{t} \equiv {\sum\limits_{i}{w_{i}\pi_{it}}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

where parameters of equation 4 are defined as follows:π_(it) probability modified by the modification factor that user i willconsume title tw_(i) weight of each user iA_(t) a prediction of a number of users that will consume media asset tas received (aggregated forecast).

In some embodiments, the viewership forecasting application may use thegenerated consumption probability metric to forecast viewership of across-section of users. Specifically, the viewership forecastingapplication may receive a characteristic associated with a group ofusers. The viewership forecasting application may compare thecharacteristic with each of a plurality of profiles associated with theplurality of users and select, based on the comparing, a set of useridentifiers from the plurality of user identifiers corresponding tothose profiles that match the characteristic. The viewership forecastingapplication may determine, using a portion of the metric associated withthe set of user identifiers, an amount of users that are likely toconsume the media asset.

The calculation may be represented by the following equation:

$\begin{matrix}{A_{t,{tgt}} = {\sum\limits_{i \in {tgt}}{w_{i}\pi_{it}}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

where parameters of equation 5 are defined as follows:π_(it) probability modified by the modification factor that user i willconsume title tw_(i) weight of each user iA_(t,tgt) a prediction of a number of users with a particularcharacteristic of preference that will consume media asset t.

For example, the viewership forecasting application may receive a textstring as a characteristic (e.g., chocolate). The viewership forecastingapplication may iterate through each user profile and compare the textof the string with data stored in each user profile. The viewershipconsumption application may store, in a data structure, an identifierassociated with each profile that matches the characteristic. Theviewership forecasting application may iterate through each identifiercorresponding to a matching profile (e.g., a user identifier 152) andretrieve a modified probability (e.g., a probability 154) associatedwith that identifier. The viewership forecasting application may add allthe probabilities for the matching profiles and multiply the result bythe weight in order to determine a cross-section of users that arepredicted to consume a media asset.

In some embodiments, the viewership forecasting application maydetermine the amount of users that are likely to consume the media assetusing the following actions. The viewership forecasting application mayretrieve (e.g., from data structure 150) the appropriate modifiedprobabilities (e.g., probabilities 154), calculate a sum of modifiedprobabilities that are associated with the portion of the user metricand multiply the sum by the weight. For example, if there are five usersin the plurality of users with the modified probabilities of 0.6, 0.7,0.4, 0.3, and 0.5 and the weight of each user is twenty million, theviewership forecasting application may calculate a sum of all theprobabilities (i.e., 2.5) and multiple the sum by the weight (i.e., 25million) to arrive at the result of 62.5 million users.

In some embodiments, the viewership forecasting application maydetermine a number of unique users that are likely to consume anadvertisement associated with a specific advertiser using the generatedconsumption probability metric. Specifically, the viewership forecastingapplication may receive, from an advertiser, a value representing anumber of advertisements associated with the advertiser that are to beplayed during presentation of the media asset and determine, based on(1) the value representing the number of advertisements and (2) theplurality of modified probabilities and (3) the weight, a number ofunique users that are likely to consume any advertisement that is both(1) associated with the advertiser and (2) is to be played during thepresentation of the media asset.

For example, for each user, the viewership forecasting application mayretrieve a first modified probability (e.g., a probability 154) andsubtract that number from one. The result of the subtraction operationis stored in a variable and is raised to the power equal to the numberof advertisements associated with the advertisers that are run duringthe media asset. The viewership forecasting application may store theresulting value in a variable. This value is subtracted from one. Theseactions determine a probability that a user consumed at least oneadvertisement associated with the advertiser that ran during the mediaasset. The viewership forecasting application may multiply the resultingprobability by the weight to determine the probability that a uniqueuser will consume at least one advertisement run within the media assetconsidered in the calculation. The viewership forecasting applicationmay make a similar determination for all users that are being consideredand calculate a sum of the result to determine how many unique users arepredicted to consume an advertisement (i.e., determine reach).

It should be noted that these values may be calculated over a pluralityof media assets (e.g., media assets in an advertising campaign). Thecalculation may be represented by the following equation:

$\begin{matrix}{R = {\sum\limits_{i}{w_{i}\left\lbrack {1 - {\underset{t \in C}{\Pi}\left( {1 - \pi_{it}} \right)}^{n}} \right\rbrack}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

where parameters of equation 6 are defined as follows:R a number of unique users predicted to consume at least one title t inthe advertising campaign Cπ_(it) probability modified by the modification factor that user i willconsume title tw_(i) weight of each user iπ_(it) probability modified by the modification factor that user i willconsume title t.

In some embodiments, the viewership forecasting application maydetermine the average number of advertisement exposures per user.

Specifically, the viewership forecasting application may determine,based on the total number of users and the number of unique users thatare likely to consume any advertisement associated with the advertiser,a number of times that each user consumed an advertisement that isassociated with the advertiser. For example, the viewership forecastingapplication may calculate the number of times on average each userconsumed an advertisement by dividing a total number of users thatconsumed the advertisement by the number of unique users that consumedthe advertisement. The calculation may be represented by the followingequation:

$\begin{matrix}{f = \frac{\Sigma_{t \in C}A_{t}}{R}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

where parameters of equation 7 are defined as follows:R a number of unique users predicted to consume at least one title t inthe advertising campaign CA_(t) a prediction of a number of users that will consume media asset tas received (aggregated forecast)f an average number of times(frequency) a user saw each of titles t thatare part of the advertising campaign c.

In some embodiments, the viewership forecasting application may receivethe first number of users that are predicted to consume the media assetwith the following actions. The viewership forecasting application mayreceive a media asset identifier associated with the media asset. Theviewership forecasting application may transmit, to an audiencemeasurement provider, a request for the first number of users that arepredicted to consume the media asset, where the request includes themedia asset identifier and receive, in response to the request, thefirst number of users that are predicted to consume the media asset. Forexample, a user may provider the viewership forecasting application witha media asset identifier for consumption probability metric generation.The viewership forecasting application may transmit the media assetidentifier to an audio measurement service and receive back a number ofusers predicted to consume the media asset associated with the mediaasset identifier.

The viewership forecasting application may receive the plurality ofprobabilities with the following actions. The viewership forecastingapplication may select a service that stores a plurality of profilesassociated with the plurality of users and transmit, to a profile serverassociated with the service, a request for the plurality ofprobabilities, the request including the media asset identifier. Theviewership forecasting application may receive, in response to therequest, the plurality of probabilities. The viewership forecastingapplication may store the received information in a data structure withmultiple fields. For example, the fields may be designed to accommodatethe different types of information in the profiles. The viewershipforecasting application may select a provider based on a number of usersfor which profiles exist, the amount of data in those profiles, oranother suitable criterion. In some embodiments, the viewershipforecasting application may select multiple providers. The viewershipforecasting application may transmit a request for the plurality ofprobabilities.

For example, the viewership forecasting application may transmit a mediaasset identifier associated with the media asset requesting probabilityfor each user consuming the media asset and receive in response theappropriate probabilities. In some embodiments, the viewershipforecasting application may receive a plurality of user profiles andbased on an algorithm determine a probability of each user associatedwith a corresponding profile consuming the media asset.

In some embodiments, the viewership forecasting application maydetermine, based on (1) the weight for the plurality of users and (2)the plurality of probabilities, the second number of users that arepredicted to consume the media asset with the following actions. Theviewership forecasting application may compute a sum of the plurality ofprobabilities and multiply the sum of the plurality of probabilities bythe weight.

In some embodiments, the viewership forecasting application may computethe modification factor for the first media asset by dividing the firstnumber of users by the second number of users. For example, if a Nielsenrating for a media asset is predicted to be twenty million users of onehundred million users (aggregated forecast) and the number of usersdetermined from the plurality of probabilities is twenty-five millionusers, the modification factor may be calculated as twenty-five milliondivided by twenty million (i.e., 1.25).

In some embodiments, the viewership forecasting application maygenerate, for the media asset, the metric comprising (1) a plurality ofuser identifiers associated with the plurality of users and (2) aplurality of modified probabilities with the following actions. Theviewership forecasting application may select a first probability of theplurality of probabilities and compute a product of the firstprobability and the modification factor. The viewership forecastingapplication may store, in a data structure associated with the metric,the product of the first probability and the modification factor and theuser identifier associated with the first probability.

The amount of content available to users in any given content deliverysystem can be substantial. Consequently, many users desire a form ofmedia guidance through an interface that allows users to efficientlynavigate content selections and easily identify content that they maydesire. An application that provides such guidance is referred to hereinas an interactive media guidance application or, sometimes, a mediaguidance application or a guidance application.

Interactive media guidance applications may take various forms dependingon the content for which they provide guidance. One typical type ofmedia guidance application is an interactive television program guide.Interactive television program guides (sometimes referred to aselectronic program guides) are well-known guidance applications that,among other things, allow users to navigate among and locate many typesof content or media assets. Interactive media guidance applications maygenerate graphical user interface screens that enable a user to navigateamong, locate and select content. As referred to herein, the terms“media asset” and “content” should be understood to mean anelectronically consumable user asset, such as television programming, aswell as pay-per-view programs, on-demand programs (as in video-on-demand(VOD) systems), Internet content (e.g., streaming content, downloadablecontent, Webcasts, etc.), video clips, audio, content information,pictures, rotating images, documents, playlists, websites, articles,books, electronic books, blogs, advertisements, chat sessions, socialmedia, applications, games, and/or any other media or multimedia and/orcombination of the same. Guidance applications also allow users tonavigate among and locate content. As referred to herein, the term“multimedia” should be understood to mean content that utilizes at leasttwo different content forms described above, for example, text, audio,images, video, or interactivity content forms. Content may be recorded,played, displayed or accessed by user equipment devices, but can also bepart of a live performance.

The media guidance application and/or any instructions for performingany of the embodiments discussed herein may be encoded on computerreadable media. Computer readable media includes any media capable ofstoring data. The computer readable media may be transitory, including,but not limited to, propagating electrical or electromagnetic signals,or may be non-transitory including, but not limited to, volatile andnon-volatile computer memory or storage devices such as a hard disk,floppy disk, USB drive, DVD, CD, media cards, register memory, processorcaches, Random Access Memory (“RAM”), etc.

With the advent of the Internet, mobile computing, and high-speedwireless networks, users are accessing media on user equipment deviceson which they traditionally did not. As referred to herein, the phrase“user equipment device,” “user equipment,” “user device,” “electronicdevice,” “electronic equipment,” “media equipment device,” or “mediadevice” should be understood to mean any device for accessing thecontent described above, such as a television, a Smart TV, a set-topbox, an integrated receiver decoder (IRD) for handling satellitetelevision, a digital storage device, a digital media receiver (DMR), adigital media adapter (DMA), a streaming media device, a DVD player, aDVD recorder, a connected DVD, a local media server, a BLU-RAY player, aBLU-RAY recorder, a personal computer (PC), a laptop computer, a tabletcomputer, a WebTV box, a personal computer television (PC/TV), a PCmedia server, a PC media center, a hand-held computer, a stationarytelephone, a personal digital assistant (PDA), a mobile telephone, aportable video player, a portable music player, a portable gamingmachine, a smart phone, or any other television equipment, computingequipment, or wireless device, and/or combination of the same. In someembodiments, the user equipment device may have a front facing screenand a rear facing screen, multiple front screens, or multiple angledscreens. In some embodiments, the user equipment device may have a frontfacing camera and/or a rear facing camera. On these user equipmentdevices, users may be able to navigate among and locate the same contentavailable through a television. Consequently, media guidance may beavailable on these devices, as well. The guidance provided may be forcontent available only through a television, for content available onlythrough one or more of other types of user equipment devices, or forcontent available both through a television and one or more of the othertypes of user equipment devices. The media guidance applications may beprovided as on-line applications (i.e., provided on a web-site), or asstand-alone applications or clients on user equipment devices. Variousdevices and platforms that may implement media guidance applications aredescribed in more detail below.

One of the functions of the media guidance application is to providemedia guidance data to users. As referred to herein, the phrase “mediaguidance data” or “guidance data” should be understood to mean any datarelated to content or data used in operating the guidance application.For example, the guidance data may include program information, guidanceapplication settings, user preferences, user profile information, medialistings, media-related information (e.g., broadcast times, broadcastchannels, titles, descriptions, ratings information (e.g., parentalcontrol ratings, critic's ratings, etc.), genre or category information,actor information, logo data for broadcasters' or providers' logos,etc.), media format (e.g., standard definition, high definition, 3D,etc.), advertisement information (e.g., text, images, media clips,etc.), on-demand information, blogs, websites, and any other type ofguidance data that is helpful for a user to navigate among and locatedesired content selections.

FIGS. 2-3 show illustrative display screens that may be used to providemedia guidance data. The display screens shown in FIGS. 2-3 may beimplemented on any suitable user equipment device or platform. While thedisplays of FIGS. 2-3 are illustrated as full screen displays, they mayalso be fully or partially overlaid over content being displayed. A usermay indicate a desire to access content information by selecting aselectable option provided in a display screen (e.g., a menu option, alistings option, an icon, a hyperlink, etc.) or pressing a dedicatedbutton (e.g., a GUIDE button) on a remote control or other user inputinterface or device. In response to the user's indication, the mediaguidance application may provide a display screen with media guidancedata organized in one of several ways, such as by time and channel in agrid, by time, by channel, by source, by content type, by category(e.g., movies, sports, news, children, or other categories ofprogramming), or other predefined, user-defined, or other organizationcriteria.

FIG. 2 shows illustrative grid of a program listings display 200arranged by time and channel that also enables access to different typesof content in a single display. Display 200 may include grid 202 with(1) a column of channel/content type identifiers 204, where eachchannel/content type identifier (which is a cell in the column)identifies a different channel or content type available; and (2) a rowof time identifiers 206, where each time identifier (which is a cell inthe row) identifies a time block of programming. Grid 202 also includescells of program listings, such as program listing 208, where eachlisting provides the title of the program provided on the listing'sassociated channel and time. With a user input device, a user can selectprogram listings by moving highlight region 210. Information relating tothe program listing selected by highlight region 210 may be provided inprogram information region 212. Region 212 may include, for example, theprogram title, the program description, the time the program is provided(if applicable), the channel the program is on (if applicable), theprogram's rating, and other desired information.

In addition to providing access to linear programming (e.g., contentthat is scheduled to be transmitted to a plurality of user equipmentdevices at a predetermined time and is provided according to aschedule), the media guidance application also provides access tonon-linear programming (e.g., content accessible to a user equipmentdevice at any time and is not provided according to a schedule).Non-linear programming may include content from different contentsources including on-demand content (e.g., VOD), Internet content (e.g.,streaming media, downloadable media, etc.), locally stored content(e.g., content stored on any user equipment device described above orother storage device), or other time-independent content. On-demandcontent may include movies or any other content provided by a particularcontent provider (e.g., HBO On Demand providing “The Sopranos” and “CurbYour Enthusiasm”). HBO ON DEMAND is a service mark owned by Time WarnerCompany L.P. et al. and THE SOPRANOS and CURB YOUR ENTHUSIASM aretrademarks owned by the Home Box Office, Inc. Internet content mayinclude web events, such as a chat session or Webcast, or contentavailable on-demand as streaming content or downloadable content throughan Internet web site or other Internet access (e.g. FTP).

Grid 202 may provide media guidance data for non-linear programmingincluding on-demand listing 214, recorded content listing 216, andInternet content listing 218. A display combining media guidance datafor content from different types of content sources is sometimesreferred to as a “mixed-media” display. Various permutations of thetypes of media guidance data that may be displayed that are differentthan display 200 may be based on user selection or guidance applicationdefinition (e.g., a display of only recorded and broadcast listings,only on-demand and broadcast listings, etc.). As illustrated, listings214, 216, and 218 are shown as spanning the entire time block displayedin grid 202 to indicate that selection of these listings may provideaccess to a display dedicated to on-demand listings, recorded listings,or Internet listings, respectively. In some embodiments, listings forthese content types may be included directly in grid 202. Additionalmedia guidance data may be displayed in response to the user selectingone of the navigational icons 220. (Pressing an arrow key on a userinput device may affect the display in a similar manner as selectingnavigational icons 220.)

Display 200 may also include video region 222, advertisement 224, andoptions region 226. Video region 222 may allow the user to view and/orpreview programs that are currently available, will be available, orwere available to the user. The content of video region 222 maycorrespond to, or be independent from, one of the listings displayed ingrid 202. Grid displays including a video region are sometimes referredto as picture-in-guide (PIG) displays. PIG displays and theirfunctionalities are described in greater detail in Satterfield et al.U.S. Pat. No. 6,564,378, issued May 13, 2003 and Yuen et al. U.S. Pat.No. 6,239,794, issued May 29, 2001, which are hereby incorporated byreference herein in their entireties. PIG displays may be included inother media guidance application display screens of the embodimentsdescribed herein.

Advertisement 224 may provide an advertisement for content that,depending on a viewer's access rights (e.g., for subscriptionprogramming), is currently available for viewing, will be available forviewing in the future, or may never become available for viewing, andmay correspond to or be unrelated to one or more of the content listingsin grid 202. Advertisement 224 may also be for products or servicesrelated or unrelated to the content displayed in grid 202. Advertisement224 may be selectable and provide further information about content,provide information about a product or a service, enable purchasing ofcontent, a product, or a service, provide content relating to theadvertisement, etc. Advertisement 224 may be targeted based on a user'sprofile/preferences, monitored user activity, the type of displayprovided, or on other suitable targeted advertisement bases.

While advertisement 224 is shown as rectangular or banner shaped,advertisements may be provided in any suitable size, shape, and locationin a guidance application display. For example, advertisement 224 may beprovided as a rectangular shape that is horizontally adjacent to grid202. This is sometimes referred to as a panel advertisement. Inaddition, advertisements may be overlaid over content or a guidanceapplication display or embedded within a display. Advertisements mayalso include text, images, rotating images, video clips, or other typesof content described above. Advertisements may be stored in a userequipment device having a guidance application, in a database connectedto the user equipment, in a remote location (including streaming mediaservers), or on other storage means, or a combination of theselocations. Providing advertisements in a media guidance application isdiscussed in greater detail in, for example, Knudson et al., U.S. PatentApplication Publication No. 2003/0110499, filed Jan. 17, 2003; Ward, IIIet al. U.S. Pat. No. 6,756,997, issued Jun. 29, 2004; and Schein et al.U.S. Pat. No. 6,388,714, issued May 14, 2002, which are herebyincorporated by reference herein in their entireties. It will beappreciated that advertisements may be included in other media guidanceapplication display screens of the embodiments described herein.

Options region 226 may allow the user to access different types ofcontent, media guidance application displays, and/or media guidanceapplication features. Options region 226 may be part of display 200 (andother display screens described herein), or may be invoked by a user byselecting an on-screen option or pressing a dedicated or assignablebutton on a user input device. The selectable options within optionsregion 226 may concern features related to program listings in grid 202or may include options available from a main menu display. Featuresrelated to program listings may include searching for other air times orways of receiving a program, recording a program, enabling seriesrecording of a program, setting program and/or channel as a favorite,purchasing a program, or other features. Options available from a mainmenu display may include search options, VOD options, parental controloptions, Internet options, cloud-based options, device synchronizationoptions, second screen device options, options to access various typesof media guidance data displays, options to subscribe to a premiumservice, options to edit a user's profile, options to access a browseoverlay, or other options.

The media guidance application may be personalized based on a user'spreferences. A personalized media guidance application allows a user tocustomize displays and features to create a personalized “experience”with the media guidance application. This personalized experience may becreated by allowing a user to input these customizations and/or by themedia guidance application monitoring user activity to determine varioususer preferences. Users may access their personalized guidanceapplication by logging in or otherwise identifying themselves to theguidance application. Customization of the media guidance applicationmay be made in accordance with a user profile. The customizations mayinclude varying presentation schemes (e.g., color scheme of displays,font size of text, etc.), aspects of content listings displayed (e.g.,only HDTV or only 3D programming, user-specified broadcast channelsbased on favorite channel selections, re-ordering the display ofchannels, recommended content, etc.), desired recording features (e.g.,recording or series recordings for particular users, recording quality,etc.), parental control settings, customized presentation of Internetcontent (e.g., presentation of social media content, e-mail,electronically delivered articles, etc.) and other desiredcustomizations.

The media guidance application may allow a user to provide user profileinformation or may automatically compile user profile information. Themedia guidance application may, for example, monitor the content theuser accesses and/or other interactions the user may have with theguidance application. Additionally, the media guidance application mayobtain all or part of other user profiles that are related to aparticular user (e.g., from other web sites on the Internet the useraccesses, such as www.Tivo.com, from other media guidance applicationsthe user accesses, from other interactive applications the useraccesses, from another user equipment device of the user, etc.), and/orobtain information about the user from other sources that the mediaguidance application may access. As a result, a user can be providedwith a unified guidance application experience across the user'sdifferent user equipment devices. This type of user experience isdescribed in greater detail below in connection with FIG. 5. Additionalpersonalized media guidance application features are described ingreater detail in Ellis et al., U.S. Patent Application Publication No.2005/0251827, filed Jul. 11, 2005, Boyer et al., U.S. Pat. No.7,165,098, issued Jan. 16, 2007, and Ellis et al., U.S. PatentApplication Publication No. 2002/0174430, filed Feb. 21, 2002, which arehereby incorporated by reference herein in their entireties.

Another display arrangement for providing media guidance is shown inFIG. 3. Video mosaic display 300 includes selectable options 302 forcontent information organized based on content type, genre, and/or otherorganization criteria. In display 300, television listings option 304 isselected, thus providing listings 306, 308, 310, and 312 as broadcastprogram listings. In display 300 the listings may provide graphicalimages including cover art, still images from the content, video clippreviews, live video from the content, or other types of content thatindicate to a user the content being described by the media guidancedata in the listing. Each of the graphical listings may also beaccompanied by text to provide further information about the contentassociated with the listing. For example, listing 308 may include morethan one portion, including media portion 314 and text portion 316.Media portion 314 and/or text portion 316 may be selectable to viewcontent in full-screen or to view information related to the contentdisplayed in media portion 314 (e.g., to view listings for the channelthat the video is displayed on).

The listings in display 300 are of different sizes (i.e., listing 306 islarger than listings 308, 310, and 312), but if desired, all thelistings may be the same size. Listings may be of different sizes orgraphically accentuated to indicate degrees of interest to the user orto emphasize certain content, as desired by the content provider orbased on user preferences. Various systems and methods for graphicallyaccentuating content listings are discussed in, for example, Yates, U.S.Patent Application Publication No. 2010/0153885, filed Nov. 12, 2009,which is hereby incorporated by reference herein in its entirety.

Users may access content and the media guidance application (and itsdisplay screens described above and below) from one or more of theiruser equipment devices. FIG. 4 shows a generalized embodiment ofillustrative user equipment device 400.

More specific implementations of user equipment devices are discussedbelow in connection with FIG. 5. User equipment device 400 may receivecontent and data via input/output (hereinafter “I/O”) path 402. I/O path402 may provide content (e.g., broadcast programming, on-demandprogramming, Internet content, content available over a local areanetwork (LAN) or wide area network (WAN), and/or other content) and datato control circuitry 404, which includes processing circuitry 406 andstorage 408. Control circuitry 404 may be used to send and receivecommands, requests, and other suitable data using I/O path 402. I/O path402 may connect control circuitry 404 (and specifically processingcircuitry 406) to one or more communications paths (described below).I/O functions may be provided by one or more of these communicationspaths, but are shown as a single path in FIG. 4 to avoidovercomplicating the drawing.

Control circuitry 404 may be based on any suitable processing circuitrysuch as processing circuitry 406. As referred to herein, processingcircuitry should be understood to mean circuitry based on one or moremicroprocessors, microcontrollers, digital signal processors,programmable logic devices, field-programmable gate arrays (FPGAs),application-specific integrated circuits (ASICs), etc., and may includea multi-core processor (e.g., dual-core, quad-core, hexa-core, or anysuitable number of cores) or supercomputer. In some embodiments,processing circuitry may be distributed across multiple separateprocessors or processing units, for example, multiple of the same typeof processing units (e.g., two Intel Core i7 processors) or multipledifferent processors (e.g., an Intel Core i5 processor and an Intel Corei7 processor). In some embodiments, control circuitry 404 executesinstructions for a media guidance application stored in memory (i.e.,storage 408). Specifically, control circuitry 404 may be instructed bythe media guidance application to perform the functions discussed aboveand below. For example, the media guidance application may provideinstructions to control circuitry 404 to generate the media guidancedisplays. In some implementations, any action performed by controlcircuitry 404 may be based on instructions received from the mediaguidance application.

In client-server based embodiments, control circuitry 404 may includecommunications circuitry suitable for communicating with a guidanceapplication server or other networks or servers. The instructions forcarrying out the above mentioned functionality may be stored on theguidance application server. Communications circuitry may include acable modem, an integrated services digital network (ISDN) modem, adigital subscriber line (DSL) modem, a telephone modem, Ethernet card,or a wireless modem for communications with other equipment, or anyother suitable communications circuitry. Such communications may involvethe Internet or any other suitable communications networks or paths(which is described in more detail in connection with FIG. 5). Inaddition, communications circuitry may include circuitry that enablespeer-to-peer communication of user equipment devices, or communicationof user equipment devices in locations remote from each other (describedin more detail below).

Memory may be an electronic storage device provided as storage 408 thatis part of control circuitry 404. As referred to herein, the phrase“electronic storage device” or “storage device” should be understood tomean any device for storing electronic data, computer software, orfirmware, such as random-access memory, read-only memory, hard drives,optical drives, digital video disc (DVD) recorders, compact disc (CD)recorders, BLU-RAY disc (BD) recorders, BLU-RAY 3D disc recorders,digital video recorders (DVR, sometimes called a personal videorecorder, or PVR), solid state devices, quantum storage devices, gamingconsoles, gaming media, or any other suitable fixed or removable storagedevices, and/or any combination of the same. Storage 408 may be used tostore various types of content described herein as well as mediaguidance data described above. Nonvolatile memory may also be used(e.g., to launch a boot-up routine and other instructions). Cloud-basedstorage, described in relation to FIG. 5, may be used to supplementstorage 408 or instead of storage 408.

Control circuitry 404 may include video generating circuitry and tuningcircuitry, such as one or more analog tuners, one or more MPEG-2decoders or other digital decoding circuitry, high-definition tuners, orany other suitable tuning or video circuits or combinations of suchcircuits. Encoding circuitry (e.g., for converting over-the-air, analog,or digital signals to MPEG signals for storage) may also be provided.Control circuitry 404 may also include scaler circuitry for upconvertingand downconverting content into the preferred output format of the userequipment 400. Circuitry 404 may also include digital-to-analogconverter circuitry and analog-to-digital converter circuitry forconverting between digital and analog signals. The tuning and encodingcircuitry may be used by the user equipment device to receive and todisplay, to play, or to record content. The tuning and encodingcircuitry may also be used to receive guidance data. The circuitrydescribed herein, including for example, the tuning, video generating,encoding, decoding, encrypting, decrypting, scaler, and analog/digitalcircuitry, may be implemented using software running on one or moregeneral purpose or specialized processors. Multiple tuners may beprovided to handle simultaneous tuning functions (e.g., watch and recordfunctions, picture-in-picture (PIP) functions, multiple-tuner recording,etc.). If storage 408 is provided as a separate device from userequipment 400, the tuning and encoding circuitry (including multipletuners) may be associated with storage 408.

A user may send instructions to control circuitry 404 using user inputinterface 410. User input interface 410 may be any suitable userinterface, such as a remote control, mouse, trackball, keypad, keyboard,touch screen, touchpad, stylus input, joystick, voice recognitioninterface, or other user input interfaces. Display 412 may be providedas a stand-alone device or integrated with other elements of userequipment device 400. For example, display 412 may be a touchscreen ortouch-sensitive display. In such circumstances, user input interface 410may be integrated with or combined with display 412. Display 412 may beone or more of a monitor, a television, a liquid crystal display (LCD)for a mobile device, amorphous silicon display, low temperature polysilicon display, electronic ink display, electrophoretic display, activematrix display, electro-wetting display, electrofluidic display, cathoderay tube display, light-emitting diode display, electroluminescentdisplay, plasma display panel, high-performance addressing display,thin-film transistor display, organic light-emitting diode display,surface-conduction electron-emitter display (SED), laser television,carbon nanotubes, quantum dot display, interferometric modulatordisplay, or any other suitable equipment for displaying visual images.In some embodiments, display 412 may be HDTV-capable. In someembodiments, display 412 may be a 3D display, and the interactive mediaguidance application and any suitable content may be displayed in 3D. Avideo card or graphics card may generate the output to the display 412.The video card may offer various functions such as accelerated renderingof 3D scenes and 2D graphics, MPEG-2/MPEG-4 decoding, TV output, or theability to connect multiple monitors. The video card may be anyprocessing circuitry described above in relation to control circuitry404. The video card may be integrated with the control circuitry 404.Speakers 414 may be provided as integrated with other elements of userequipment device 400 or may be stand-alone units. The audio component ofvideos and other content displayed on display 412 may be played throughspeakers 414. In some embodiments, the audio may be distributed to areceiver (not shown), which processes and outputs the audio via speakers414.

The guidance application may be implemented using any suitablearchitecture. For example, it may be a stand-alone application whollyimplemented on user equipment device 400. In such an approach,instructions of the application are stored locally (e.g., in storage408), and data for use by the application is downloaded on a periodicbasis (e.g., from an out-of-band feed, from an Internet resource, orusing another suitable approach). Control circuitry 404 may retrieveinstructions of the application from storage 408 and process theinstructions to generate any of the displays discussed herein. Based onthe processed instructions, control circuitry 404 may determine whataction to perform when input is received from input interface 410. Forexample, movement of a cursor on a display up/down may be indicated bythe processed instructions when input interface 410 indicates that anup/down button was selected.

In some embodiments, the media guidance application is a client-serverbased application. Data for use by a thick or thin client implemented onuser equipment device 400 is retrieved on-demand by issuing requests toa server remote to the user equipment device 400. In one example of aclient-server based guidance application, control circuitry 404 runs aweb browser that interprets web pages provided by a remote server. Forexample, the remote server may store the instructions for theapplication in a storage device. The remote server may process thestored instructions using circuitry (e.g., control circuitry 404) andgenerate the displays discussed above and below. The client device mayreceive the displays generated by the remote server and may display thecontent of the displays locally on equipment device 400. This way, theprocessing of the instructions is performed remotely by the server whilethe resulting displays are provided locally on equipment device 400.Equipment device 400 may receive inputs from the user via inputinterface 410 and transmit those inputs to the remote server forprocessing and generating the corresponding displays. For example,equipment device 400 may transmit a communication to the remote serverindicating that an up/down button was selected via input interface 410.The remote server may process instructions in accordance with that inputand generate a display of the application corresponding to the input(e.g., a display that moves a cursor up/down). The generated display isthen transmitted to equipment device 400 for presentation to the user.

In some embodiments, the media guidance application is downloaded andinterpreted or otherwise run by an interpreter or virtual machine (runby control circuitry 404). In some embodiments, the guidance applicationmay be encoded in the ETV Binary Interchange Format (EBIF), received bycontrol circuitry 404 as part of a suitable feed, and interpreted by auser agent running on control circuitry 404. For example, the guidanceapplication may be an EBIF application. In some embodiments, theguidance application may be defined by a series of JAVA-based files thatare received and run by a local virtual machine or other suitablemiddleware executed by control circuitry 404. In some of suchembodiments (e.g., those employing MPEG-2 or other digital mediaencoding schemes), the guidance application may be, for example, encodedand transmitted in an MPEG-2 object carousel with the MPEG audio andvideo packets of a program.

User equipment device 400 of FIG. 4 can be implemented in system 500 ofFIG. 5 as user television equipment 502, user computer equipment 504,wireless user communications device 506, or any other type of userequipment suitable for accessing content, such as a non-portable gamingmachine. For simplicity, these devices may be referred to hereincollectively as user equipment or user equipment devices, and may besubstantially similar to user equipment devices described above. Userequipment devices, on which a media guidance application may beimplemented, may function as a standalone device or may be part of anetwork of devices. Various network configurations of devices may beimplemented and are discussed in more detail below.

A user equipment device utilizing at least some of the system featuresdescribed above in connection with FIG. 4 may not be classified solelyas user television equipment 502, user computer equipment 504, or awireless user communications device 506. For example, user televisionequipment 502 may, like some user computer equipment 504, beInternet-enabled allowing for access to Internet content, while usercomputer equipment 504 may, like some user television equipment 502,include a tuner allowing for access to television programming. The mediaguidance application may have the same layout on various different typesof user equipment or may be tailored to the display capabilities of theuser equipment. For example, on user computer equipment 504, theguidance application may be provided as a web site accessed by a webbrowser. In another example, the guidance application may be scaled downfor wireless user communications devices 506.

In system 500, there is typically more than one of each type of userequipment device but only one of each is shown in FIG. 5 to avoidovercomplicating the drawing. In addition, each user may utilize morethan one type of user equipment device and also more than one of eachtype of user equipment device.

In some embodiments, a user equipment device (e.g., user televisionequipment 502, user computer equipment 504, wireless user communicationsdevice 506) may be referred to as a “second screen device.” For example,a second screen device may supplement content presented on a first userequipment device. The content presented on the second screen device maybe any suitable content that supplements the content presented on thefirst device. In some embodiments, the second screen device provides aninterface for adjusting settings and display preferences of the firstdevice. In some embodiments, the second screen device is configured forinteracting with other second screen devices or for interacting with asocial network. The second screen device can be located in the same roomas the first device, a different room from the first device but in thesame house or building, or in a different building from the firstdevice.

The user may also set various settings to maintain consistent mediaguidance application settings across in-home devices and remote devices.Settings include those described herein, as well as channel and programfavorites, programming preferences that the guidance applicationutilizes to make programming recommendations, display preferences, andother desirable guidance settings. For example, if a user sets a channelas a favorite on, for example, the web site www.Tivo.com on theirpersonal computer at their office, the same channel would appear as afavorite on the user's in-home devices (e.g., user television equipmentand user computer equipment) as well as the user's mobile devices, ifdesired. Therefore, changes made on one user equipment device can changethe guidance experience on another user equipment device, regardless ofwhether they are the same or a different type of user equipment device.In addition, the changes made may be based on settings input by a user,as well as user activity monitored by the guidance application.

The user equipment devices may be coupled to communications network 514.Namely, user television equipment 502, user computer equipment 504, andwireless user communications device 506 are coupled to communicationsnetwork 514 via communications paths 508, 510, and 512, respectively.Communications network 514 may be one or more networks including theInternet, a mobile phone network, mobile voice or data network (e.g., a4G or LTE network), cable network, public switched telephone network, orother types of communications network or combinations of communicationsnetworks. Paths 508, 510, and 512 may separately or together include oneor more communications paths, such as, a satellite path, a fiber-opticpath, a cable path, a path that supports Internet communications (e.g.,IPTV), free-space connections (e.g., for broadcast or other wirelesssignals), or any other suitable wired or wireless communications path orcombination of such paths. Path 512 is drawn with dotted lines toindicate that in the exemplary embodiment shown in FIG. 5 it is awireless path and paths 508 and 510 are drawn as solid lines to indicatethey are wired paths (although these paths may be wireless paths, ifdesired). Communications with the user equipment devices may be providedby one or more of these communications paths, but are shown as a singlepath in FIG. 5 to avoid overcomplicating the drawing.

Although communications paths are not drawn between user equipmentdevices, these devices may communicate directly with each other viacommunication paths, such as those described above in connection withpaths 508, 510, and 512, as well as other short-range point-to-pointcommunication paths, such as USB cables, IEEE 1394 cables, wirelesspaths (e.g., Bluetooth, infrared, IEEE 802-11x, etc.), or othershort-range communication via wired or wireless paths. BLUETOOTH is acertification mark owned by Bluetooth SIG, INC. The user equipmentdevices may also communicate with each other directly through anindirect path via communications network 514.

System 500 includes content source 516 and media guidance data source518 coupled to communications network 514 via communication paths 520and 522, respectively. Paths 520 and 522 may include any of thecommunication paths described above in connection with paths 508, 510,and 512. Communications with the content source 516 and media guidancedata source 518 may be exchanged over one or more communications paths,but are shown as a single path in FIG. 5 to avoid overcomplicating thedrawing. In addition, there may be more than one of each of contentsource 516 and media guidance data source 518, but only one of each isshown in FIG. 5 to avoid overcomplicating the drawing. (The differenttypes of each of these sources are discussed below.) If desired, contentsource 516 and media guidance data source 518 may be integrated as onesource device. Although communications between sources 516 and 518 withuser equipment devices 502, 504, and 506 are shown as throughcommunications network 514, in some embodiments, sources 516 and 518 maycommunicate directly with user equipment devices 502, 504, and 506 viacommunication paths (not shown) such as those described above inconnection with paths 508, 510, and 512.

Content source 516 may include one or more types of content distributionequipment including a television distribution facility, cable systemheadend, satellite distribution facility, programming sources (e.g.,television broadcasters, such as NBC, ABC, HBO, etc.), intermediatedistribution facilities and/or servers, Internet providers, on-demandmedia servers, and other content providers. NBC is a trademark owned bythe National Broadcasting Company, Inc., ABC is a trademark owned by theAmerican Broadcasting Company, Inc., and HBO is a trademark owned by theHome Box Office, Inc. Content source 516 may be the originator ofcontent (e.g., a television broadcaster, a Webcast provider, etc.) ormay not be the originator of content (e.g., an on-demand contentprovider, an Internet provider of content of broadcast programs fordownloading, etc.). Content source 516 may include cable sources,satellite providers, on-demand providers, Internet providers,over-the-top content providers, or other providers of content. Contentsource 516 may also include a remote media server used to storedifferent types of content (including video content selected by a user),in a location remote from any of the user equipment devices. Systems andmethods for remote storage of content, and providing remotely storedcontent to user equipment are discussed in greater detail in connectionwith Ellis et al., U.S. Pat. No. 7,761,892, issued Jul. 20, 2010, whichis hereby incorporated by reference herein in its entirety.

Media guidance data source 518 may provide media guidance data, such asthe media guidance data described above. Media guidance data may beprovided to the user equipment devices using any suitable approach. Insome embodiments, the guidance application may be a stand-aloneinteractive television program guide that receives program guide datavia a data feed (e.g., a continuous feed or trickle feed). Programschedule data and other guidance data may be provided to the userequipment on a television channel sideband, using an in-band digitalsignal, using an out-of-band digital signal, or by any other suitabledata transmission technique. Program schedule data and other mediaguidance data may be provided to user equipment on multiple analog ordigital television channels.

In some embodiments, guidance data from media guidance data source 518may be provided to users' equipment using a client-server approach. Forexample, a user equipment device may pull media guidance data from aserver, or a server may push media guidance data to a user equipmentdevice. In some embodiments, a guidance application client residing onthe user's equipment may initiate sessions with source 518 to obtainguidance data when needed, e.g., when the guidance data is out of dateor when the user equipment device receives a request from the user toreceive data. Media guidance may be provided to the user equipment withany suitable frequency (e.g., continuously, daily, a user-specifiedperiod of time, a system-specified period of time, in response to arequest from user equipment, etc.). Media guidance data source 518 mayprovide user equipment devices 502, 504, and 506 the media guidanceapplication itself or software updates for the media guidanceapplication.

In some embodiments, the media guidance data may include viewer data.For example, the viewer data may include current and/or historical useractivity information (e.g., what content the user typically watches,what times of day the user watches content, whether the user interactswith a social network, at what times the user interacts with a socialnetwork to post information, what types of content the user typicallywatches (e.g., pay TV or free TV), mood, brain activity information,etc.). The media guidance data may also include subscription data. Forexample, the subscription data may identify to which sources or servicesa given user subscribes and/or to which sources or services the givenuser has previously subscribed but later terminated access (e.g.,whether the user subscribes to premium channels, whether the user hasadded a premium level of services, whether the user has increasedInternet speed). In some embodiments, the viewer data and/or thesubscription data may identify patterns of a given user for a period ofmore than one year. The media guidance data may include a model (e.g., asurvivor model) used for generating a score that indicates a likelihooda given user will terminate access to a service/source. For example, themedia guidance application may process the viewer data with thesubscription data using the model to generate a value or score thatindicates a likelihood of whether the given user will terminate accessto a particular service or source. In particular, a higher score mayindicate a higher level of confidence that the user will terminateaccess to a particular service or source. Based on the score, the mediaguidance application may generate promotions and advertisements thatentice the user to keep the particular service or source indicated bythe score as one to which the user will likely terminate access.

Media guidance applications may be, for example, stand-aloneapplications implemented on user equipment devices. For example, themedia guidance application may be implemented as software or a set ofexecutable instructions which may be stored in storage 408, and executedby control circuitry 404 of a user equipment device 400. In someembodiments, media guidance applications may be client-serverapplications where only a client application resides on the userequipment device, and server application resides on a remote server. Forexample, media guidance applications may be implemented partially as aclient application on control circuitry 404 of user equipment device 400and partially on a remote server as a server application (e.g., mediaguidance data source 518) running on control circuitry of the remoteserver. When executed by control circuitry of the remote server (such asmedia guidance data source 518), the media guidance application mayinstruct the control circuitry to generate the guidance applicationdisplays and transmit the generated displays to the user equipmentdevices. The server application may instruct the control circuitry ofthe media guidance data source 518 to transmit data for storage on theuser equipment.

The client application may instruct control circuitry of the receivinguser equipment to generate the guidance application displays.

Content and/or media guidance data delivered to user equipment devices502, 504, and 506 may be over-the-top (OTT) content. OTT contentdelivery allows Internet-enabled user devices, including any userequipment device described above, to receive content that is transferredover the Internet, including any content described above, in addition tocontent received over cable or satellite connections. OTT content isdelivered via an Internet connection provided by an Internet serviceprovider (ISP), but a third party distributes the content. The ISP maynot be responsible for the viewing abilities, copyrights, orredistribution of the content, and may only transfer IP packets providedby the OTT content provider. Examples of OTT content providers includeYOUTUBE, NETFLIX, and HULU, which provide audio and video via IPpackets. Youtube is a trademark owned by Google Inc., Netflix is atrademark owned by Netflix Inc., and Hulu is a trademark owned by Hulu,LLC. OTT content providers may additionally or alternatively providemedia guidance data described above. In addition to content and/or mediaguidance data, providers of OTT content can distribute media guidanceapplications (e.g., web-based applications or cloud-based applications),or the content can be displayed by media guidance applications stored onthe user equipment device.

Media guidance system 500 is intended to illustrate a number ofapproaches, or network configurations, by which user equipment devicesand sources of content and guidance data may communicate with each otherfor the purpose of accessing content and providing media guidance. Theembodiments described herein may be applied in any one or a subset ofthese approaches, or in a system employing other approaches fordelivering content and providing media guidance. The following fourapproaches provide specific illustrations of the generalized example ofFIG. 5.

In one approach, user equipment devices may communicate with each otherwithin a home network. User equipment devices can communicate with eachother directly via short-range point-to-point communication schemesdescribed above, via indirect paths through a hub or other similardevice provided on a home network, or via communications network 514.Each of the multiple individuals in a single home may operate differentuser equipment devices on the home network. As a result, it may bedesirable for various media guidance information or settings to becommunicated between the different user equipment devices. For example,it may be desirable for users to maintain consistent media guidanceapplication settings on different user equipment devices within a homenetwork, as described in greater detail in Ellis et al., U.S. PatentPublication No. 2005/0251827, filed Jul. 11, 2005. Different types ofuser equipment devices in a home network may also communicate with eachother to transmit content. For example, a user may transmit content fromuser computer equipment to a portable video player or portable musicplayer.

In a second approach, users may have multiple types of user equipment bywhich they access content and obtain media guidance. For example, someusers may have home networks that are accessed by in-home and mobiledevices. Users may control in-home devices via a media guidanceapplication implemented on a remote device. For example, users mayaccess an online media guidance application on a website via a personalcomputer at their office, or a mobile device such as a PDA orweb-enabled mobile telephone. The user may set various settings (e.g.,recordings, reminders, or other settings) on the online guidanceapplication to control the user's in-home equipment. The online guidemay control the user's equipment directly, or by communicating with amedia guidance application on the user's in-home equipment. Varioussystems and methods for user equipment devices communicating, where theuser equipment devices are in locations remote from each other, isdiscussed in, for example, Ellis et al., U.S. Pat. No. 8,046,801, issuedOct. 25, 2011, which is hereby incorporated by reference herein in itsentirety.

In a third approach, users of user equipment devices inside and outsidea home can use their media guidance application to communicate directlywith content source 516 to access content. Specifically, within a home,users of user television equipment 502 and user computer equipment 504may access the media guidance application to navigate among and locatedesirable content. Users may also access the media guidance applicationoutside of the home using wireless user communications devices 506 tonavigate among and locate desirable content.

In a fourth approach, user equipment devices may operate in a cloudcomputing environment to access cloud services. In a cloud computingenvironment, various types of computing services for content sharing,storage or distribution (e.g., video sharing sites or social networkingsites) are provided by a collection of network-accessible computing andstorage resources, referred to as “the cloud.” For example, the cloudcan include a collection of server computing devices, which may belocated centrally or at distributed locations, that provide cloud-basedservices to various types of users and devices connected via a networksuch as the Internet via communications network 514. These cloudresources may include one or more content sources 516 and one or moremedia guidance data sources 518. In addition or in the alternative, theremote computing sites may include other user equipment devices, such asuser television equipment 502, user computer equipment 504, and wirelessuser communications device 506. For example, the other user equipmentdevices may provide access to a stored copy of a video or a streamedvideo. In such embodiments, user equipment devices may operate in apeer-to-peer manner without communicating with a central server.

The cloud provides access to services, such as content storage, contentsharing, or social networking services, among other examples, as well asaccess to any content described above, for user equipment devices.Services can be provided in the cloud through cloud computing serviceproviders, or through other providers of online services. For example,the cloud-based services can include a content storage service, acontent sharing site, a social networking site, or other services viawhich user-sourced content is distributed for viewing by others onconnected devices. These cloud-based services may allow a user equipmentdevice to store content to the cloud and to receive content from thecloud rather than storing content locally and accessing locally-storedcontent.

A user may use various content capture devices, such as camcorders,digital cameras with video mode, audio recorders, mobile phones, andhandheld computing devices, to record content. The user can uploadcontent to a content storage service on the cloud either directly, forexample, from user computer equipment 504 or wireless usercommunications device 506 having content capture feature. Alternatively,the user can first transfer the content to a user equipment device, suchas user computer equipment 504. The user equipment device storing thecontent uploads the content to the cloud using a data transmissionservice on communications network 514. In some embodiments, the userequipment device itself is a cloud resource, and other user equipmentdevices can access the content directly from the user equipment deviceon which the user stored the content.

Cloud resources may be accessed by a user equipment device using, forexample, a web browser, a media guidance application, a desktopapplication, a mobile application, and/or any combination of accessapplications of the same. The user equipment device may be a cloudclient that relies on cloud computing for application delivery, or theuser equipment device may have some functionality without access tocloud resources. For example, some applications running on the userequipment device may be cloud applications, i.e., applications deliveredas a service over the Internet, while other applications may be storedand run on the user equipment device. In some embodiments, a user devicemay receive content from multiple cloud resources simultaneously. Forexample, a user device can stream audio from one cloud resource whiledownloading content from a second cloud resource. Or a user device candownload content from multiple cloud resources for more efficientdownloading. In some embodiments, user equipment devices can use cloudresources for processing operations such as the processing operationsperformed by processing circuitry described in relation to FIG. 4.

As referred herein, the term “in response to” refers to initiated as aresult of. For example, a first action being performed in response to asecond action may include interstitial steps between the first actionand the second action. As referred herein, the term “directly inresponse to” refers to caused by. For example, a first action beingperformed directly in response to a second action may not includeinterstitial steps between the first action and the second action.

It should be noted that the viewership forecasting application may beexecuted on any of user device 502, 504, and/or 506. The viewershipforecasting application may also be executed on a server (e.g., mediacontent source 516 and/or media guidance data source 518). Theviewership forecasting application may be executed using controlcircuitry 404.

FIG. 6 is a flowchart of illustrative actions for generating aconsumption probability metric for a media asset. At 602, controlcircuitry 404 receives a first number of users that are predicted toconsume a media asset, where the first number of users is a portion of atotal number of users. For example, the control circuitry may receivethe first number of users via I/O path 402 and store the received firstnumber in storage 408.

At 604, control circuitry 404 retrieves a plurality of probabilities,each corresponding to a user in a plurality of users, where eachprobability indicates how likely a respective user is to consume themedia asset. For example, the control circuitry may transmit a requestvia I/O path 402 and receive the plurality of probabilities via I/O path402 from a remote server (e.g., media content source 516 and/or mediaguidance data source 518). It should be noted that in some embodimentscontrol circuitry 404 may reside on a server (e.g., media content source516 and/or media guidance data source 518) and receive that plurality ofprobabilities from another remote server.

At 606, control circuitry 404 calculates a weight for the plurality ofusers, where the weight represents a ratio of the total number of usersto a number of users in the plurality of users. For example, the controlcircuitry may perform a mathematical operation to perform thecalculation. At 608, control circuitry 404 determines, based on (1) theweight for the plurality of users and (2) the plurality ofprobabilities, a second number of users that are predicted to consumethe media asset. For example, the control circuitry may perform amathematical operation to complete the calculation.

At 610, control circuitry 404 computes, based on the first number ofusers and the second number of users, a modification factor for thefirst media asset. For example, the control circuitry may perform amathematical operation to complete the calculation.

At 612, control circuitry 404 generates, for the media asset, a metriccomprising (1) a plurality of user identifiers associated with theplurality of users and (2) a plurality of modified probabilities,wherein each modified probability of the plurality of modifiedprobabilities is modified by the modification factor. For example, thecontrol circuitry may iterate through each probability and apply themodification factor. The result may be stored in storage 408. In someembodiments, the result may be stored at a remote server (e.g., mediacontent source 516 and/or media guidance data source 518).

FIG. 7 is a flowchart of illustrative actions for using a consumptionprobability metric to determine a number of users associated with acharacteristic that are predicted to consume a media asset. At 702,control circuitry 404 receives a characteristic associated with a groupof users. The control circuitry may receive the characteristic via I/Opath 402 of FIG. 4. In some embodiments, the control circuitry mayreceive the characteristic via user input interface 410 (FIG. 4). Thecharacteristic may be one or more of text, image, sound (e.g., voiceinput) or a combination of these.

At 704, control circuitry 404 selects a previously unselected profile.For example, a data structure containing a plurality of profiles for aplurality of users may be stored in storage 408. The control circuitrymay access the data structure and iterate through the profiles in thedata structure to select previously unselected profiles. In someembodiments, the profiles may be stored in a database on a remote serverand the control circuitry may select the profiles by transmitting adatabase query to the server for the user identifiers associated withthe profiles, and, using the user identifiers, request (e.g., throughI/O path 402 via communications network 524) the selected profile. Thecontrol circuitry may store the received profile in a data structure instorage 408.

At 706, control circuitry 404 compares the selected profile with thecharacteristic. The control circuitry may execute a textual comparison(e.g., string comparison) between the characteristic and any text in theselected profile. In some embodiments, the control circuitry may includesynonyms of the characteristic with the profile. The control circuitrymay retrieve the synonyms from any dictionary or thesaurus service.

At 708, control circuitry 404 determines whether the selected profilematches the characteristic. For example, the control circuitry maydetermine a match if at least one word in the user profile matches thecharacteristic. In some embodiments, the control circuitry may determinea match if a synonym of the characteristic matches a word in the userprofile. If control circuitry 404 determines that the selected profilematches the characteristic, process 700 moves to 710. At 710, controlcircuitry 404 adds a user associated with the selected profile to theset of users. For example, the control circuitry may store (e.g., instorage 408) a data containing user identifiers corresponding to userthat match the characteristic. The control circuitry may add a useridentifier corresponding to the selected user to the data structure.

If control circuitry 404 determines that the selected profile does notmatch the characteristic, process 700 moves to 712. At 712, controlcircuitry 404 determines whether there are any more previouslyunselected profiles. For example, if the control circuitry is iteratingthrough all the profiles in the data structure, the control circuitrymay determine if it is at the end of the data structure. Additionally oralternatively, the control circuitry may be iterating through the datastructure using a looping mechanism where the loop ends when the lastprofile is selected. If control circuitry 404 determines that there aremore previously unselected profiles, process 700 moves to 704. Ifcontrol circuitry 404 determines that there are no more previouslyunselected profiles, process 700 moves to 714.

At 714, control circuitry 404 retrieves for the set of users a set ofcorresponding modified probabilities. To continue with the aboveexample, if the control circuitry has stored a data structure thatincludes a user identifier corresponding to user profiles that match thecharacteristic, the control circuitry may access the data structure(e.g., in storage 408) and retrieve from the data structure thecorresponding modified probabilities.

At 716, control circuitry 404 calculates a sum of the set ofcorresponding modified probabilities. For example, the control circuitrymay create a variable that is to store the sum of the modifiedprobabilities retrieved in 714. At 718, control circuitry 404 computes aproduct of the sum and a weight associated with the plurality of users.The control circuitry may execute a mathematical multiplication functionto perform the calculation.

FIG. 8 is a flowchart of illustrative actions for using a consumptionprobability metric to determine a unique reach for one or moreadvertisements within a media asset. At 802, control circuitry 404receives, from an advertiser, a value representing a number ofadvertisements associated with the advertiser that are to be playedduring presentation of the media asset. For example, the controlcircuitry may receive the value via I/O path 402. In some embodiments,the control circuitry may receive the value via user input interface410.

At 804, control circuitry 404 selects a previously unselected user fromthe plurality of users. For example, the control circuitry may beiterating through all the users in the plurality of users. The pluralityof users may be stored in a data structure (e.g., in storage 408). Thecontrol circuitry may determine if it is at the end of the datastructure. Additionally or alternatively, the control circuitry may beiterating through the data structure using a looping mechanism where theloop ends when the last user is selected.

At 806, control circuitry 404 retrieves a modified probabilityassociated with the selected user. For example, the control circuitrymay access the data structure associated with the user and retrieve thecorresponding probability. If the data structure is located at a remoteserver, the control circuitry may transmit a request to the remoteserver for the probability and receive the probability in response.

At 808, control circuitry 404 calculates a difference between one andthe modified probability raised to a power corresponding to theretrieved number of advertisements. For example, the control circuitrymay use a mathematical function to make the calculations. At 810,control circuitry 404 stores, in a data structure, a difference betweenthe result and one multiplied by the weight. For example, the controlcircuitry may make the calculation and store the result in a variable(e.g., in storage 408).

At 810, control circuitry 404 determines whether there are any morepreviously unselected users in the plurality of users. If controlcircuitry 404 determines that there are more previously unselected usersin the plurality of users, process 800 moves to 804. If controlcircuitry 404 determines that there are no more previously unselectedusers in the plurality of users, process 800 moves to 814. At 814,control circuitry 404 computes a sum of all results stored in the datastructure. For example, the control circuitry may execute an additionoperation on the results.

It is contemplated that the steps or descriptions of each of FIGS. 6-8may be used with any other embodiment of this disclosure. In addition,the steps and descriptions described in relation to FIGS. 6-8 may bedone in alternative orders or in parallel to further the purposes ofthis disclosure. For example, each of these actions may be performed inany order or in parallel or substantially simultaneously to reduce lagor increase the speed of the system or method. Furthermore, it should benoted that any of the devices or equipment discussed in relation toFIGS. 4-5 may be used to perform one or more of the steps in FIGS. 6-8.

While some portions of this disclosure may make reference to“convention,” any such reference is merely for the purpose of providingcontext to the invention(s) of the instant disclosure, and does not formany admission as to what constitutes the state of the art.

The processes discussed above are intended to be illustrative and notlimiting. One skilled in the art would appreciate that the actions ofthe processes discussed herein may be omitted, modified, combined,and/or rearranged, and any additional actions may be performed withoutdeparting from the scope of the disclosure. Furthermore, it should benoted that the features and limitations described in any one embodimentmay be applied to any other embodiment herein, and flowcharts orexamples relating to one embodiment may be combined with any otherembodiment in a suitable manner, done in different orders, or done inparallel. In addition, the systems and methods described herein may beperformed in real time. It should also be noted that the systems and/ormethods described above may be applied to, or used in accordance with,other systems and/or methods.

1. A method for generating consumption probability metrics for mediaassets, the method comprising: receiving a first number of users thatare predicted to consume a media asset, wherein the first number ofusers is a portion of a total number of users; retrieving a plurality ofprobabilities each corresponding to a user in a plurality of users,wherein each probability indicates how likely a respective user is toconsume the media asset; calculating a weight for the plurality ofusers, wherein the weight represents a ratio of the total number ofusers to a number of users in the plurality of users; determining, basedon (1) the weight for the plurality of users and (2) the plurality ofprobabilities, a second number of users that are predicted to consumethe media asset; computing, based on the first number of users and thesecond number of users, a modification factor for the first media asset;and generating, for the media asset, a metric comprising (1) a pluralityof user identifiers associated with the plurality of users and (2) aplurality of modified probabilities, wherein each modified probabilityof the plurality of modified probabilities is modified by themodification factor.
 2. The method of claim 1 further comprising:receiving a characteristic associated with a group of users; comparingthe characteristic with each of a plurality of profiles associated withthe plurality of users; selecting, based on the comparing, a set useridentifiers from the plurality of user identifiers corresponding tothose profiles that match the characteristic; and determining, using aportion of the metric associated with the set of user identifiers, anamount of users that are likely to consume the media asset.
 3. Themethod of claim 2, wherein determining the amount of users that arelikely to consume the media asset comprises: calculating a sum ofmodified probabilities that are associated with the portion of the usermetric; and multiplying the sum by the weight.
 4. The method of claim 1,further comprising: receiving, from an advertiser, a value representinga number of advertisements associated with the advertiser that are to beplayed during presentation of the media asset; and determining, based on(1) the value representing the number of advertisements and (2) theplurality of modified probabilities and (3) the weight, a number ofunique users that are likely to consume any advertisement that is both(1) associated with the advertiser and (2) is to be played during thepresentation of the media asset.
 5. The method of claim 4, furthercomprising determining based on the total number of users and the numberof unique users that are likely to consume any advertisement associatedwith the advertiser a number of times that each user consumed anadvertisement that is associated with the advertiser.
 6. The method ofclaim 1, wherein receiving the first number of users that are predictedto consume the media asset comprises: receiving a media asset identifierassociated with the media asset; transmitting, to an audiencemeasurement provider, a request for the first number of users that arepredicted to consume the media asset, wherein the request includes themedia asset identifier; and receiving, in response to the request, thefirst number of users that are predicted to consume the media asset. 7.The method of claim 1, wherein retrieving the plurality of probabilitiescomprises: selecting a service that stores a plurality of profilesassociated with the plurality of users; transmitting, to a profileserver associated with the service, a request for the plurality ofprobabilities, wherein the request includes the media asset identifier;and receiving, in response to the request, the plurality ofprobabilities.
 8. The method of claim 1, wherein determining, based on(1) the weight for the plurality of users and (2) the plurality ofprobabilities, the second number of users that are predicted to consumethe media asset comprises: computing a sum of the plurality ofprobabilities; and multiplying the sum of the plurality of probabilitiesby the weight.
 9. The method of claim 1, wherein computing themodification factor for the first media asset comprises dividing thefirst number of users by the second number of users.
 10. The method ofclaim 1, wherein generating, for the media asset, the metric comprising(1) a plurality of user identifiers associated with the plurality ofusers and (2) a plurality of modified probabilities, comprises:selecting a first probability of the plurality of probabilities;computing a product of the first probability and the modificationfactor; and storing, in a data structure associated with the metric, theproduct of the first probability and the modification factor and theuser identifier associated with the first probability.
 11. A system forgenerating consumption probability metrics for media assets, the systemcomprising: control circuitry configured to: receive a first number ofusers that are predicted to consume a media asset, wherein the firstnumber of users is a portion of a total number of users; retrieve aplurality of probabilities each corresponding to a user in a pluralityof users, wherein each probability indicates how likely a respectiveuser is to consume the media asset; calculate a weight for the pluralityof users, wherein the weight represents a ratio of the total number ofusers to a number of users in the plurality of users; determine, basedon (1) the weight for the plurality of users and (2) the plurality ofprobabilities, a second number of users that are predicted to consumethe media asset; compute, based on the first number of users and thesecond number of users, a modification factor for the first media asset;and generate, for the media asset, a metric comprising (1) a pluralityof user identifiers associated with the plurality of users and (2) aplurality of modified probabilities, wherein each modified probabilityof the plurality of modified probabilities is modified by themodification factor.
 12. The system of claim 11 wherein the controlcircuitry is further configured to: receiving a characteristicassociated with a group of users; comparing the characteristic with eachof a plurality of profiles associated with the plurality of users;selecting, based on the comparing, a set user identifiers from theplurality of user identifiers corresponding to those profiles that matchthe characteristic; and determining, using a portion of the metricassociated with the set of user identifiers, an amount of users that arelikely to consume the media asset.
 13. The system of claim 12, whereinthe control circuitry is configured, when determining the amount ofusers that are likely to consume the media asset, to: calculate a sum ofmodified probabilities that are associated with the portion of the usermetric; and multiply the sum by the weight.
 14. The system of claim 11,wherein the control circuitry is further configured to: receive, from anadvertiser, a value representing a number of advertisements associatedwith the advertiser that are to be played during presentation of themedia asset; and determine, based on (1) the value representing thenumber of advertisements and (2) the plurality of modified probabilitiesand (3) the weight, a number of unique users that are likely to consumeany advertisement that is both (1) associated with the advertiser and(2) is to be played during the presentation of the media asset.
 15. Thesystem of claim 14, wherein the control circuitry is further configuredto determine, based on the total number of users and the number ofunique users that are likely to consume any advertisement associatedwith the advertiser, a number of times that each user consumed anadvertisement that is associated with the advertiser.
 16. The systemmethod of claim 11, wherein the control circuitry is configured, whenreceiving the first number of users that are predicted to consume themedia asset, to: receive a media asset identifier associated with themedia asset; transmit, to an audience measurement provider, a requestfor the first number of users that are predicted to consume the mediaasset, wherein the request includes the media asset identifier; andreceive, in response to the request, the first number of users that arepredicted to consume the media asset.
 17. The system of claim 11,wherein the control circuitry is configured, when retrieving theplurality of probabilities, to: select a service that stores a pluralityof profiles associated with the plurality of users; transmit, to aprofile server associated with the service, a request for the pluralityof probabilities, wherein the request includes the media assetidentifier; and receive, in response to the request, the plurality ofprobabilities.
 18. The system of claim 11, wherein the control circuitryis configured, when determining, based on (1) the weight for theplurality of users and (2) the plurality of probabilities, the secondnumber of users that are predicted to consume the media asset, to:compute a sum of the plurality of probabilities; and multiply the sum ofthe plurality of probabilities by the weight.
 19. The system of claim11, wherein the control circuitry is configured, when computing themodification factor for the first media asset, to divide the firstnumber of users by the second number of users.
 20. The system of claim11, wherein the control circuitry is configured, when generating, forthe media asset, the metric comprising (1) a plurality of useridentifiers associated with the plurality of users and (2) a pluralityof modified probabilities, to: select a first probability of theplurality of probabilities; compute a product of the first probabilityand the modification factor; and store, in a data structure associatedwith the metric, the product of the first probability and themodification factor and the user identifier associated with the firstprobability. 21.-50. (canceled)